The biases that contribute to bad decision-making

by Kate Evans / 22 August, 2018
RelatedArticlesModule - biases bad decision making

Daniel Kahneman. Photo/Getty Images

There are distinct patterns in the errors people make based on predictable biases in thinking, says behavioural economist Daniel Kahneman. 

The work of Daniel Kahneman with collaborator Amos Tversky remains among the most frequently cited in the social sciences. It earned him the Nobel Prize in Economics in 2002 and laid the foundations for behavioural economics, the study of how people actually make decisions, rather than how they ought to as rational agents. This in turn has inspired a spate of bestsellers – Freakonomics, Nudge, Black Swan and Predictably Irrational among them – and Kahneman’s own book for general readers, Thinking, Fast and Slow.

Biases that drive us

“When the handsome and confident speaker bounds onto the stage, Kahneman says, you can anticipate that the audience will judge his comments more favourably than he deserves. The availability of a diagnostic label for this bias – the halo effect – makes it easier to anticipate, recognise and understand.” Here are some common biases:

The anchoring effect: We adjust our estimates to accommodate arbitrary numbers. For example, a study of experienced German judges showed that sentencing could be influenced by first rolling a pair of dice. When they rolled a three, they sentenced a (hypothetical) shoplifter to an average of five months’ jail. If they rolled a nine, the average sentence was eight months.

The availability heuristic: We base our judgments on readily available memories. For example, Americans judge death by accident to be 300 times as likely as death by diabetes; the true figure is about 1.7. This misjudgment, Kahneman argues, reflects our taste for “novelty and poignancy”, compounded by our exposure to grisly instances in the media.

The affect heuristic: We put too much weight on judgments that are emotionally laden.

Base-rate neglect: We accept what is causally possible over what is statistically probable.

Competition neglect: We expect outcomes to be determined by our efforts alone, not the influence of competitors.

Hindsight bias: We overestimate the accuracy of our past predictions, believing that we knew it all along.

The illusion of skill: We attribute success to talent rather than luck.

The illusion of validity: We hold on to our beliefs in the face of contradictory evidence.

The planning fallacy: We plan around best-case scenarios rather than what is statistically likely.

Loss aversion: We are more averse to losses than we are attracted to equivalent gains.

Narrative fallacy: We create coherent causal stories to make sense of haphazard events.

Representativeness bias: We lean heavily on stereotypes to compensate for partial information.

The sunk-cost fallacy: We continue investing in an established project rather than focus on its future outcomes.

This article was first published in the July 28, 2018 issue of the New Zealand Listener.

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